UCLIC Research Seminar Series
Although clinical best practice suggests that affect awareness could enable more effective technological support for physical rehabilitation through personalisation to psychological needs, designers need to consider what affective states matter and how they should be tracked and addressed. In this paper, we set the standard by analysing how the major affective factors in chronic pain (pain, fear/anxiety, and low/depressed mood) interfere with everyday physical functioning. Further, based on discussion of the modality that should be used to track these states to enable technology to address them, we investigated the possibility of using movement behaviour to automatically detect the states. Using two body movement datasets on people with chronic pain, we show that movement behaviour enables very good discrimination between two emotional distress levels (F1=0.86), and three pain levels (F1=0.9). Performance remained high (F1=0.78 for two pain levels) with a reduced set of movement sensors. Finally, in an overall discussion, we suggest how technology-provided encouragement and awareness can be personalised given the capability to automatically monitor the relevant states, towards addressing the barriers that they pose. In addition, we highlight movement behaviour features to be tracked to provide technology with information necessary for such personalisation.
Temitayo Olugbade is a Research Fellow at UCL Interaction Centre. Her research focuses on automatic detection of cognitive/affective experiences and the behavioural and physiological responses to these experiences. Some of the studies she will discuss emerge from her PhD research, also at UCL. Her current role is as the main postdoc on two research projects, one of which (EnTimeMent) builds on the studies she will present. In between finishing her PhD and her current job, she worked as a research associate on the WeDraw project.